一种新型多元偏态正态混合模型及其在超大规模机器人系统路径规划中的应用
A Novel Multivariate Skew-Normal Mixture Model and Its Application in Path-Planning for Very-Large-Scale Robotic Systems
AI总结：
针对超大规模机器人系统在杂乱环境中的路径规划问题，提出多元偏态正态混合模型（SNMM）及其参数学习算法，并开发了两种基于SNMM的路径规划算法，仿真证明其优于传统高斯混合模型方法。
摘要：
本文探讨了在复杂和杂乱环境中运行的超大规模机器人系统（VLSR）的路径规划挑战。VLSR系统由众多协同工作的自主智能体或机器人组成。传统上，许多针对VLSR系统的方法是基于高斯混合模型（GMMs）开发的，其中GMMs代表智能体不断演变的空间分布，作为系统状态的宏观视图。然而，我们最近对VLSR系统的研究揭示了使用GMMs表示智能体分布的局限性，特别是在杂乱环境中。为了克服这些局限性，我们提出了一种名为偏态正态混合模型（SNMM）的新型模型来表示智能体分布。此外，我们提出了一种参数学习算法，旨在利用样本数据估计SNMM的参数。进一步地，我们开发了两种基于SNMM的路径规划算法，以引导VLSR系统穿过复杂和杂乱的环境。我们的仿真结果证明了这些算法与基于GMM的路径规划方法相比的有效性和优越性。
英文摘要：
This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. VLSR systems consist of numerous cooperative agents or robots working together autonomously. Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represent agents' evolving spatial distribution, serving as a macroscopic view of the system's state. However, our recent research into VLSR systems has unveiled limitations in using GMMs to represent agent distributions, especially in cluttered environments. To overcome these limitations, we propose a novel model called the skew-normal mixture model (SNMM) for representing agent distributions. Additionally, we present a parameter learning algorithm designed to estimate the SNMM's parameters using sample data. Furthermore, we develop two SNMM-based path-planning algorithms to guide VLSR systems through complex and cluttered environments. Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods.
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